import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				(EulerDiscreteScheduler,)
   __lowerCAmelCase           =				10







   def 						lowerCamelCase_	(     self						:						List[str]   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """num_train_timesteps""": 1100,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
    }

    config.update(**lowerCamelCase_				)
    return config







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    for timesteps in [10, 50, 100, 1000]:
     self.check_over_configs(num_train_timesteps=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,   [0.0_0_0_2, 0.0_0_2, 0.0_2]				):
     self.check_over_configs(beta_start=lowerCamelCase_   ,   beta_end=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config()
    UpperCamelCase			  =      scheduler_class(**lowerCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCamelCase			  =      sample.to(lowerCamelCase_				)

    for i, t in enumerate(scheduler.timesteps				):
     UpperCamelCase			  =      scheduler.scale_model_input(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      model(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      scheduler.step(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   generator=lowerCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

    UpperCamelCase			  =      torch.sum(torch.abs(lowerCamelCase_				)				)
    UpperCamelCase			  =      torch.mean(torch.abs(lowerCamelCase_				)				)

    assert abs(result_sum.item() - 1_0.0_8_0_7				) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1				) < 1E-3







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config(prediction_type="""v_prediction"""				)
    UpperCamelCase			  =      scheduler_class(**lowerCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma
    UpperCamelCase			  =      sample.to(lowerCamelCase_				)

    for i, t in enumerate(scheduler.timesteps				):
     UpperCamelCase			  =      scheduler.scale_model_input(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      model(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      scheduler.step(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   generator=lowerCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

    UpperCamelCase			  =      torch.sum(torch.abs(lowerCamelCase_				)				)
    UpperCamelCase			  =      torch.mean(torch.abs(lowerCamelCase_				)				)

    assert abs(result_sum.item() - 0.0_0_0_2				) < 1E-2
    assert abs(result_mean.item() - 2.2_676E-06				) < 1E-3







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config()
    UpperCamelCase			  =      scheduler_class(**lowerCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps   ,   device=lowerCamelCase_				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCamelCase			  =      sample.to(lowerCamelCase_				)

    for t in scheduler.timesteps:
     UpperCamelCase			  =      scheduler.scale_model_input(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      model(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      scheduler.step(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   generator=lowerCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

    UpperCamelCase			  =      torch.sum(torch.abs(lowerCamelCase_				)				)
    UpperCamelCase			  =      torch.mean(torch.abs(lowerCamelCase_				)				)

    assert abs(result_sum.item() - 1_0.0_8_0_7				) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1				) < 1E-3







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.scheduler_classes[0]
    UpperCamelCase			  =      self.get_scheduler_config()
    UpperCamelCase			  =      scheduler_class(**lowerCamelCase_   ,   use_karras_sigmas=lowerCamelCase_				)

    scheduler.set_timesteps(self.num_inference_steps   ,   device=lowerCamelCase_				)

    UpperCamelCase			  =      torch.manual_seed(0				)

    UpperCamelCase			  =      self.dummy_model()
    UpperCamelCase			  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    UpperCamelCase			  =      sample.to(lowerCamelCase_				)

    for t in scheduler.timesteps:
     UpperCamelCase			  =      scheduler.scale_model_input(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      model(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      scheduler.step(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   generator=lowerCamelCase_				)
     UpperCamelCase			  =      output.prev_sample

    UpperCamelCase			  =      torch.sum(torch.abs(lowerCamelCase_				)				)
    UpperCamelCase			  =      torch.mean(torch.abs(lowerCamelCase_				)				)

    assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9				) < 1E-2
    assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3				) < 1E-3

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
